48,785 research outputs found
The TREC-2002 video track report
TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open
Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview
of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings
Interactive Machine Learning with Applications in Health Informatics
Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort.
I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd
Union Leadership Development as Driver of Equity and Inclusion
Analysis generated for the research project on Young Workers and the Labor Movement highlighted the need for innovative leadership development if young workers are to be integrated into an increasingly diverse workersâ movement, and unleashed to provide leadership in established institutions that face a radically changing economy and workforce. We examine one successful leadership development program: the New York State AFL-CIO/Cornell Union Leadership Institute. We look at the theoretical underpinnings, development, and outcomes of this multi-union, multi-sector program over the past 17 years, note its impact on the New York regional labor movement, and analyze the key factors accounting for the programâs successful development of innovative-minded young labor leaders. Those factors venturing beyond the traditional âskill-building â approach of most labor leadership training toward a more transformational model of leadership development; an emphasis on experiential learning, using a variety of learning modes; providing a safe space and what Kurt Lewin describes as âa community of practiceâ where difficult challenges can be tackled collectively; and using leadership development as a tool to build inclusion and solidarity across many dimensions of difference, including age, race, gender, ethnicity, sector, able-ness, education level, industry, and more
Quantitative Perspectives on Fifty Years of the Journal of the History of Biology
Journal of the History of Biology provides a fifty-year long record for
examining the evolution of the history of biology as a scholarly discipline. In
this paper, we present a new dataset and preliminary quantitative analysis of
the thematic content of JHB from the perspectives of geography, organisms, and
thematic fields. The geographic diversity of authors whose work appears in JHB
has increased steadily since 1968, but the geographic coverage of the content
of JHB articles remains strongly lopsided toward the United States, United
Kingdom, and western Europe and has diversified much less dramatically over
time. The taxonomic diversity of organisms discussed in JHB increased steadily
between 1968 and the late 1990s but declined in later years, mirroring broader
patterns of diversification previously reported in the biomedical research
literature. Finally, we used a combination of topic modeling and nonlinear
dimensionality reduction techniques to develop a model of multi-article fields
within JHB. We found evidence for directional changes in the representation of
fields on multiple scales. The diversity of JHB with regard to the
representation of thematic fields has increased overall, with most of that
diversification occurring in recent years. Drawing on the dataset generated in
the course of this analysis, as well as web services in the emerging digital
history and philosophy of science ecosystem, we have developed an interactive
web platform for exploring the content of JHB, and we provide a brief overview
of the platform in this article. As a whole, the data and analyses presented
here provide a starting-place for further critical reflection on the evolution
of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table
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